In statistics, a probit model is a type of regression where the dependent variable can only take two values, for example married or not married. The name is from probability + unit.
A probit model is a popular specification for an ordinal or a binary response model that employs a probit link function. This model is most often estimated using standard maximum likelihood procedure, such an estimation being called a probit regression.
Probit models were introduced by Chester Bliss in 1934, and a fast method for computing maximum likelihood estimates for them was proposed by Ronald Fisher in an appendix to Bliss 1935.
Read more about Probit Model: Introduction, Maximum Likelihood Estimation, Berkson's Minimum Chi-square Method, Gibbs Sampling
Famous quotes containing the word model:
“One of the most important things we adults can do for young children is to model the kind of person we would like them to be.”
—Carol B. Hillman (20th century)